Add SetFit model
Browse files- 1_Pooling/config.json +7 -0
- README.md +219 -0
- config.json +24 -0
- config_sentence_transformers.json +7 -0
- config_setfit.json +4 -0
- model.safetensors +3 -0
- model_head.pkl +3 -0
- modules.json +14 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +51 -0
- tokenizer.json +0 -0
- tokenizer_config.json +59 -0
- vocab.txt +0 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 768,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": true,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false
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}
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README.md
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---
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library_name: setfit
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tags:
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- setfit
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- sentence-transformers
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- text-classification
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- generated_from_setfit_trainer
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metrics:
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- accuracy
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widget:
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- text: Someone comes out of the shack and shoves one of the kids to the ground. He
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- text: He approaches the object and reads a plaque on its side. Someone
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- text: Later at someone's family farm, someone sees the lights on in the hangar.
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Someone
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- text: Someone stands looking over some of the old photographs as someone goes through
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the mess on the desk. Someone
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- text: Snow blows around a city of towering crystalline structures. A warrior
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pipeline_tag: text-classification
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inference: true
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base_model: sentence-transformers/paraphrase-mpnet-base-v2
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model-index:
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- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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results:
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- task:
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type: text-classification
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name: Text Classification
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dataset:
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name: Unknown
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type: unknown
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split: test
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metrics:
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- type: accuracy
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value: 0.16557377049180327
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name: Accuracy
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---
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# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.
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The model has been trained using an efficient few-shot learning technique that involves:
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
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2. Training a classification head with features from the fine-tuned Sentence Transformer.
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## Model Details
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### Model Description
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- **Model Type:** SetFit
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
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- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
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- **Maximum Sequence Length:** 512 tokens
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- **Number of Classes:** 9 classes
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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### Model Labels
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| Label | Examples |
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|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| 6 | <ul><li>'The man claps his hands together. The man'</li><li>'Emerging in open water, he does a breaststroke toward the murky. He'</li><li>'The girl does 2 perfect flips. The girls'</li></ul> |
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| 3 | <ul><li>'The younger insurance rep solemnly faces his partner. The older man'</li><li>'He grabs her hair and pulls her head back. She'</li><li>'A kid in blue shorts is vacuuming the floor. A kid in a red shirt'</li></ul> |
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| 2 | <ul><li>'In slow motion, both the Russians and Americans celebrate. Someone'</li><li>'Through a window, we watch someone raise his teacup to his companions. At home, someone'</li><li>'As our view retracts through the star map a holographic line sets out from the gunner chair and targets hologram of the planet earth. She'</li></ul> |
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| 4 | <ul><li>"The waiter refills someone's glass. Someone"</li><li>"He finds someone's records in a box. Someone"</li><li>"Bloodstains spread over someone's white shirt. Someone"</li></ul> |
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| 7 | <ul><li>'Now, someone stands below an overcast sky. Strands of his greasy black hair'</li><li>'Someone turns at the sound of the distant horns. 6000 horsemen, lead by people,'</li><li>'Someone points his wand upwards. High above, red sparks'</li></ul> |
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| 5 | <ul><li>'Now in the eating quarters, someone faces a husky, larged - nosed cook. The cook'</li><li>'A logo for a sports even is shown. There'</li><li>'Someone stirs the cookie dough in a bowl. The dough'</li></ul> |
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| 0 | <ul><li>'He shows a water bottle he has along with a brush, and uses the brush to remove snow from the dash window of a car and the water to remove any excess snow left on the windshield. Once finished, he'</li><li>"Someone and someone step into a tent. Someone's mouth"</li><li>'Someone steps outside and opens an umbrella. Someone halts,'</li></ul> |
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| 8 | <ul><li>'Someone peers out from the cabin. As she emerges, someone'</li><li>'He gently tries to pull up and then reel the fishing line out of the hole. He'</li><li>'A woman smiles at the camera. The woman'</li></ul> |
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| 1 | <ul><li>'We see a title screen. We'</li><li>'A lot of people are sitting on terraces in a big field and people is walking in the entrance of a big stadium. men'</li><li>'We see the finished painting and a line of paints. We then'</li></ul> |
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## Evaluation
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### Metrics
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| Label | Accuracy |
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|:--------|:---------|
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| **all** | 0.1656 |
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## Uses
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### Direct Use for Inference
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First install the SetFit library:
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```bash
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pip install setfit
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```
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Then you can load this model and run inference.
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```python
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from setfit import SetFitModel
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("HelgeKn/Swag-multi-class-8")
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# Run inference
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preds = model("He approaches the object and reads a plaque on its side. Someone")
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```
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<!--
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### Downstream Use
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*List how someone could finetune this model on their own dataset.*
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-->
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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### Training Set Metrics
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| Training set | Min | Median | Max |
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|:-------------|:----|:--------|:----|
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| Word count | 6 | 14.0833 | 40 |
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| Label | Training Sample Count |
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|:------|:----------------------|
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| 0 | 8 |
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| 1 | 8 |
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| 2 | 8 |
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| 3 | 8 |
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| 4 | 8 |
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| 5 | 8 |
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| 6 | 8 |
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| 7 | 8 |
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| 8 | 8 |
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### Training Hyperparameters
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- batch_size: (16, 16)
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- num_epochs: (2, 2)
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- max_steps: -1
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- sampling_strategy: oversampling
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- num_iterations: 20
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- body_learning_rate: (2e-05, 2e-05)
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- head_learning_rate: 2e-05
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- loss: CosineSimilarityLoss
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- distance_metric: cosine_distance
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- margin: 0.25
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- end_to_end: False
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- use_amp: False
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- warmup_proportion: 0.1
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- seed: 42
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- eval_max_steps: -1
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- load_best_model_at_end: False
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:------:|:----:|:-------------:|:---------------:|
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| 0.0056 | 1 | 0.2013 | - |
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| 0.2778 | 50 | 0.1955 | - |
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| 0.5556 | 100 | 0.0693 | - |
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| 0.8333 | 150 | 0.0166 | - |
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| 1.1111 | 200 | 0.0369 | - |
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| 1.3889 | 250 | 0.0149 | - |
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| 1.6667 | 300 | 0.0095 | - |
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| 1.9444 | 350 | 0.0238 | - |
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### Framework Versions
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- Python: 3.9.13
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- SetFit: 1.0.1
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- Sentence Transformers: 2.2.2
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- Transformers: 4.36.0
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- PyTorch: 2.1.1+cpu
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- Datasets: 2.15.0
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- Tokenizers: 0.15.0
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## Citation
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### BibTeX
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```bibtex
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@article{https://doi.org/10.48550/arxiv.2209.11055,
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doi = {10.48550/ARXIV.2209.11055},
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url = {https://arxiv.org/abs/2209.11055},
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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title = {Efficient Few-Shot Learning Without Prompts},
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publisher = {arXiv},
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year = {2022},
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copyright = {Creative Commons Attribution 4.0 International}
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}
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```
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<!--
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## Glossary
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*Clearly define terms in order to be accessible across audiences.*
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-->
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<!--
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## Model Card Authors
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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-->
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<!--
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## Model Card Contact
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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-->
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config.json
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{
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"_name_or_path": "C:\\Users\\Man_f/.cache\\torch\\sentence_transformers\\sentence-transformers_paraphrase-mpnet-base-v2\\",
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"architectures": [
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"MPNetModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": 0,
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"eos_token_id": 2,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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"model_type": "mpnet",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 1,
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"relative_attention_num_buckets": 32,
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"torch_dtype": "float32",
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"transformers_version": "4.36.0",
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"vocab_size": 30527
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}
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config_sentence_transformers.json
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{
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"__version__": {
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"sentence_transformers": "2.0.0",
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"transformers": "4.7.0",
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"pytorch": "1.9.0+cu102"
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}
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}
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config_setfit.json
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{
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"normalize_embeddings": false,
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"labels": null
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:cc7287d62faf553bdf8bedf59124354ea8328bfbcc40968cc297c32f465c672a
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size 437967672
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model_head.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:757e52ac05fdad93cc9a4a0103ddb315a6fa5d2c70972beafd82e56cb2b847bf
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size 29204
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modules.json
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.models.Transformer"
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},
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+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
}
|
14 |
+
]
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
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|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,51 @@
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|
1 |
+
{
|
2 |
+
"bos_token": {
|
3 |
+
"content": "<s>",
|
4 |
+
"lstrip": false,
|
5 |
+
"normalized": false,
|
6 |
+
"rstrip": false,
|
7 |
+
"single_word": false
|
8 |
+
},
|
9 |
+
"cls_token": {
|
10 |
+
"content": "<s>",
|
11 |
+
"lstrip": false,
|
12 |
+
"normalized": true,
|
13 |
+
"rstrip": false,
|
14 |
+
"single_word": false
|
15 |
+
},
|
16 |
+
"eos_token": {
|
17 |
+
"content": "</s>",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"mask_token": {
|
24 |
+
"content": "<mask>",
|
25 |
+
"lstrip": true,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
},
|
30 |
+
"pad_token": {
|
31 |
+
"content": "<pad>",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": false,
|
35 |
+
"single_word": false
|
36 |
+
},
|
37 |
+
"sep_token": {
|
38 |
+
"content": "</s>",
|
39 |
+
"lstrip": false,
|
40 |
+
"normalized": true,
|
41 |
+
"rstrip": false,
|
42 |
+
"single_word": false
|
43 |
+
},
|
44 |
+
"unk_token": {
|
45 |
+
"content": "[UNK]",
|
46 |
+
"lstrip": false,
|
47 |
+
"normalized": false,
|
48 |
+
"rstrip": false,
|
49 |
+
"single_word": false
|
50 |
+
}
|
51 |
+
}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,59 @@
|
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|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {
|
3 |
+
"0": {
|
4 |
+
"content": "<s>",
|
5 |
+
"lstrip": false,
|
6 |
+
"normalized": false,
|
7 |
+
"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"1": {
|
12 |
+
"content": "<pad>",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"2": {
|
20 |
+
"content": "</s>",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"104": {
|
28 |
+
"content": "[UNK]",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"30526": {
|
36 |
+
"content": "<mask>",
|
37 |
+
"lstrip": true,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
}
|
43 |
+
},
|
44 |
+
"bos_token": "<s>",
|
45 |
+
"clean_up_tokenization_spaces": true,
|
46 |
+
"cls_token": "<s>",
|
47 |
+
"do_basic_tokenize": true,
|
48 |
+
"do_lower_case": true,
|
49 |
+
"eos_token": "</s>",
|
50 |
+
"mask_token": "<mask>",
|
51 |
+
"model_max_length": 512,
|
52 |
+
"never_split": null,
|
53 |
+
"pad_token": "<pad>",
|
54 |
+
"sep_token": "</s>",
|
55 |
+
"strip_accents": null,
|
56 |
+
"tokenize_chinese_chars": true,
|
57 |
+
"tokenizer_class": "MPNetTokenizer",
|
58 |
+
"unk_token": "[UNK]"
|
59 |
+
}
|
vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|